Systems and methods for staffing master controller

Abstract

Apparatuses, systems, and methods described herein include receiving by a quantum hybrid branch simulation engine, a request for a next task by a branch personnel or worker. The branch simulation engine may receive data representing a simulated branch environment and apply a deep reinforcement learning (DRL) staffing operations model to predict a next action or task. The model may weigh impacts of a reward to substantially maximize the reward in light of a policy to determine the next action and update a database to store/represent a next state of the simulated or real-life branch environment after the next action is performed.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 receiving by a branch simulation engine, from a database, a first dataset that represents a simulated branch environment;   applying by the branch simulation engine, a deep reinforcement learning (DRL) staffing operations model to the first dataset to predict a next action or task for a branch worker, wherein the DRL staffing operations model weighs impacts of a reward to substantially maximize the reward in light of a policy to determine the next action;   iteratively updating, by the branch simulation engine, the database that includes the first dataset to include a second dataset to represent a next state of the simulated branch environment after the next action is performed;   providing one or more policies including the policy or instructions learned by the branch simulation engine from the DRL staffing operations model to a master DRL agent, wherein the master DRL agent is configured to recommend a real-life action in a real-life branch environment and comprises at least a first neural network (NN) and a second neural network (NN), wherein the first NN is coupled to receive an input from the second NN, and wherein the second NN is a classical NN that computes a real-life reward and a probability of a long-term real-life reward from the real-life action, wherein the master DRL agent generates an output configured to cause a computing device to dispatch a digital work assignment corresponding to a selected real-life action determined based on the computed probability of the real-life reward or long-term real-life reward.   
     
     
         2 . The computer-implemented method of  claim 1  wherein the branch simulation engine computes and ranks a plurality of next possible actions or tasks that are performed by a simulated branch worker according to the policy. 
     
     
         3 . The computer-implemented method of  claim 1  wherein the branch simulation engine comprises a quantum hybrid simulation engine including classical computing devices to assist in preparing data coupled to a quantum computing device that assists in determining an effect of the action or task on the simulated branch environment. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the policy is determined by a policy machine of the branch simulation engine trained to output a next action for a real-life branch agent, where inputs to the policy machine include data representing a current real-world branch environment. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the policy comprises a policy of a staffing organization and/or branch and includes variables representing objectives and values such as, at least, one or more of maximizing profit, worker safety, customer satisfaction, or environmental impact. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the first and the second dataset include attributes representing a branch environment comprising one or more of open job orders, high value or lower value customers, worker availability, customer preferences, or current pricing strategies. 
     
     
         7 . The computer-implemented method of  claim 1  wherein a reward network of the DRL staffing operations model implements a reward function and updates the staffing operations model based upon a computed reward value impact on the policy, including a policy of the staffing organization. 
     
     
         8 . A system, comprising:
 one or more processors; and   a memory that stores machine-readable instructions that when executed by the one or more processors cause the system to:   receive a request to generate an instruction for a next task in a real-life branch environment;   access a first dataset including attributes of a stored simulated branch environment similar to a second dataset of attributes of the real-life branch environment; based at least in part on the first dataset and the second dataset, request or cause generation of a next action or sequence of actions for a real-life worker by a master staffing engine, wherein the master staffing engine is trained on policies and instructions learned from a branch simulation engine that predicts a plurality of simulated branch environments similar to the real-life branch environment, wherein the master staffing engine comprises a master deep reinforcement learning (DRL) agent that includes at least a first neural network (NN) and a second neural network (NN) and wherein the first NN is coupled to receive an input from the second NN and wherein the second NN is a classical NN that computer a real-life reward and a probability of a long-term real-life reward from the next action in the real-life branch environment;   generate, for transmission to a computing device used by the real-life worker, a data structure that causes the computer device to render a dynamically updated work queue on a display, wherein the work queue comprises an ordered list of potential real-life actions, and wherein a priority of each of the potential real-life actions within the ordered list is determined based on the computed real-life reward or probability of the long-term real-life reward.   
     
     
         9 . The system of  claim 8  wherein the classical neural network is trained via reinforcement learning to determine a reward based on a reward function related to customer satisfaction, high-value customer satisfaction, worker retention, customer complaints, or worker utilization. 
     
     
         10 . The system of  claim 8  wherein the one or more processors are coupled to or includes a quantum processing unit (QPU). 
     
     
         11 . The system of  claim 8  wherein the system includes a hybrid-classical quantum neural network including a quantum portion including quantum layers. 
     
     
         12 . A method, comprising:
 receiving a request from a real-life worker or branch to generate by a master staffing engine, an instruction for a next task in a real-life branch environment;   loading into the master staffing engine, a first dataset including attributes of a stored simulated branch environment similar to a second dataset of attributes of the real-life branch environment;   based at least in part on the first dataset and the second dataset, causing generation by the master staffing engine, of a next action or sequence of actions for the real-life worker, wherein the master staffing engine is trained on policies and instructions learned from a branch simulation engine that predicts a plurality of simulated branch environments similar to the real-life branch environment, wherein the master staffing engine comprises a master DRL agent that includes at least a first neural network (NN) and a second neural network (NN) and wherein the first NN is coupled to receive an input from the second NN and wherein the second NN is a classical NN that computer a real-life reward and a probability of a long-term real-life reward from the next action in the real-life branch environment; and   generating an output configured to cause a computing device to dispatch a digital work assignment corresponding to a selected real-life action determined based on the computer probability of the real-life reward or long-term real-life reward.   
     
     
         13 . The method of  claim 12 , wherein the real-life reward is computed using a reward function based upon a real-life result. 
     
     
         14 . The method of  claim 12 , further comprising comparing the real-life branch environment to the stored simulated branch environment before loading the first dataset. 
     
     
         15 . The method of  claim 12 , wherein the sequence of actions comprise real-life next actions of an ordered list including actions allocated to be performed by the real-life worker and bots. 
     
     
         16 . The method of  claim 12 , wherein real-life observations of a result in the real-life branch environment are used to update a model of the master staffing engine. 
     
     
         17 . The method of  claim 12 , wherein the branch simulation engine comprises a quantum hybrid simulation engine including classical computing devices to assist in preparing data coupled to a quantum computing device that assists in determining an effect of the action or task on a first simulated branch environment.